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Results 1 - 4 of 4 for window_dimensions (0.43 sec)

  2. github.com/tensorflow/tensorflow

    tensorflow/compiler/jit/xla_ops_on_regular_devices.cc 

                              XlaCompileOnDemandOp);                               \
  REGISTER_KERNEL_BUILDER(Name("XlaReduceWindow")                              \
                              .HostMemory("window_dimensions")                 \
                              .HostMemory("window_strides")                    \
                              .HostMemory("base_dilations")                    \
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Fri Aug 19 19:55:14 UTC 2022
    - 8.8K bytes
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  3. github.com/tensorflow/tensorflow

    tensorflow/compiler/mlir/lite/stablehlo/tests/optimize_layout.mlir 

    // CHECK-SAME:          %[[PAD_VAL:.*]]: tensor<f32>) -> tensor<1x64x56x56xf32> {
// CHECK:           %[[REDUCE:.*]] = "stablehlo.reduce_window"(%[[INPUT]], %[[PAD_VAL]])
// CHECK:               <{window_dimensions = array<i64: 1, 3, 3, 1>,
// CHECK:                 window_strides = array<i64: 1, 2, 2, 1>}> ({
// CHECK:           ^bb0(%[[ARG0:.*]]: tensor<f32>, %[[ARG1:.*]]: tensor<f32>):
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Thu Apr 25 21:59:06 UTC 2024
    - 2.8K bytes
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  4. github.com/tensorflow/tensorflow

    tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/convert_func_to_bfloat16.mlir 

        ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>):
      %2 = stablehlo.maximum %arg1, %arg2 : tensor<f32>
      stablehlo.return %2 : tensor<f32>
  }) {padding = dense<[[0, 0], [1, 1], [1, 1], [0, 0]]> : tensor<4x2xi64>, window_dimensions = array<i64: 1, 3, 3, 1>} : (tensor<2x3x1x3xf32>, tensor<f32>) -> tensor<2x3x1x3xf32>
  return %1 : tensor<2x3x1x3xf32>
}

// -----
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Thu Feb 08 22:40:14 UTC 2024
    - 6K bytes
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  5. github.com/tensorflow/tensorflow

    tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/prepare_quantize/prepare_quantize.mlir 

      ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>):
    %7 = stablehlo.maximum %arg1, %arg2 : tensor<f32>
    stablehlo.return %7 : tensor<f32>
  }) {padding = dense<[[0, 0], [0, 1], [0, 1], [0, 0]]> : tensor<4x2xi64>, window_dimensions = array<i64: 1, 3, 3, 1>, window_strides = array<i64: 1, 2, 2, 1>} : (tensor<?x112x112x64xf32>, tensor<f32>) -> tensor<?x56x56x64xf32>
  return %6 : tensor<?x56x56x64xf32>
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Thu Feb 22 19:52:06 UTC 2024
    - 8.7K bytes
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